Apply it to your data 3

Which of your stock beat the market in 2022

# Load packages
library(tidyverse)
library(tidyquant)

1 Get stock prices and convert to returns

Ra <- c("VOO", "META", "NKE") %>%
    tq_get(get = "stock.prices",
           from = "2022-01-01") %>%
    group_by(symbol) %>%
    tq_transmute(select     = adjusted,
                 mutate_fun = periodReturn,
                 period     = "monthly",
                 col_rename = "Ra")
Ra

## # A tibble: 171 × 3
## # Groups:   symbol [3]
##    symbol date             Ra
##    <chr>  <date>        <dbl>
##  1 VOO    2022-01-31 -0.0582 
##  2 VOO    2022-02-28 -0.0298 
##  3 VOO    2022-03-31  0.0379 
##  4 VOO    2022-04-29 -0.0878 
##  5 VOO    2022-05-31  0.00259
##  6 VOO    2022-06-30 -0.0826 
##  7 VOO    2022-07-29  0.0920 
##  8 VOO    2022-08-31 -0.0413 
##  9 VOO    2022-09-30 -0.0920 
## 10 VOO    2022-10-31  0.0812 
## # ℹ 161 more rows

2 Get baseline and convert to returns

Rb <- "^IXIC" %>%
    tq_get(get  = "stock.prices",
           from = "2022-01-01") %>%
    tq_transmute(select     = adjusted,
                 mutate_fun = periodReturn,
                 period     = "monthly",
                 col_rename = "Rb")
Rb

## # A tibble: 57 × 2
##    date            Rb
##    <date>       <dbl>
##  1 2022-01-31 -0.101 
##  2 2022-02-28 -0.0343
##  3 2022-03-31  0.0341
##  4 2022-04-29 -0.133 
##  5 2022-05-31 -0.0205
##  6 2022-06-30 -0.0871
##  7 2022-07-29  0.123 
##  8 2022-08-31 -0.0464
##  9 2022-09-30 -0.105 
## 10 2022-10-31  0.0390
## # ℹ 47 more rows

3 Join the two tables

RaRb <- left_join (Ra, Rb, by = c("date" = "date"))
RaRb

## # A tibble: 171 × 4
## # Groups:   symbol [3]
##    symbol date             Ra      Rb
##    <chr>  <date>        <dbl>   <dbl>
##  1 VOO    2022-01-31 -0.0582  -0.101 
##  2 VOO    2022-02-28 -0.0298  -0.0343
##  3 VOO    2022-03-31  0.0379   0.0341
##  4 VOO    2022-04-29 -0.0878  -0.133 
##  5 VOO    2022-05-31  0.00259 -0.0205
##  6 VOO    2022-06-30 -0.0826  -0.0871
##  7 VOO    2022-07-29  0.0920   0.123 
##  8 VOO    2022-08-31 -0.0413  -0.0464
##  9 VOO    2022-09-30 -0.0920  -0.105 
## 10 VOO    2022-10-31  0.0812   0.0390
## # ℹ 161 more rows

4 Calculate CAPM

RaRb_capm <- RaRb %>%
  tq_performance(Ra = Ra,
                 Rb = Rb,
                 performance_fun = table.CAPM)
RaRb_capm

## # A tibble: 3 × 18
## # Groups:   symbol [3]
##   symbol ActivePremium   Alpha AlphaRobust AnnualizedAlpha  Beta `Beta-`
##   <chr>          <dbl>   <dbl>       <dbl>           <dbl> <dbl>   <dbl>
## 1 VOO           0.0021  0.0026      0.002           0.0312 0.719   0.779
## 2 META          0.0657  0.01        0.0171          0.127  1.11    0.952
## 3 NKE          -0.381  -0.0271     -0.0208         -0.281  0.581   0.796
## # ℹ 11 more variables: `Beta-Robust` <dbl>, `Beta+` <dbl>, `Beta+Robust` <dbl>,
## #   BetaRobust <dbl>, Correlation <dbl>, `Correlationp-value` <dbl>,
## #   InformationRatio <dbl>, `R-squared` <dbl>, `R-squaredRobust` <dbl>,
## #   TrackingError <dbl>, TreynorRatio <dbl>

Which stock has a positively skewed distribution of returns?

RaRb_capm <- RaRb %>%
  tq_performance(Ra = Ra,
                 Rb = Rb,
                 performance_fun = VolatilitySkewness)
RaRb_capm

## # A tibble: 3 × 2
## # Groups:   symbol [3]
##   symbol VolatilitySkewness.1
##   <chr>                 <dbl>
## 1 VOO                  11.6  
## 2 META                  1.93 
## 3 NKE                   0.731